With the recent advances in computing technology, many businesses have begun to maintain detailed records of all aspects of business operation, particularly data concerning transactions. This data may be used, inter alia, to determine which products or services are moving well, which products or services should be discontinued, packaged together, sold at the same retail outlet, etc. It can be readily appreciated that thorough analysis of transaction data can be used by businesses to more effectively control and distribute inventory and create effective store displays. For example, if a retail store sells both beer and nuts, it would be helpful from a marketing standpoint to know if there was an association rule expressing the percentage of customers buying beer who also buy nuts. Specifically, an association rule captures the notion of a set of data items occurring together in transactions. For example, in a database of a retail store which sells beer and nuts an association rule might be of the form:
beer.fwdarw.nuts (support: 3%, confidence: 87%),
which indicates that 3% of all transactions stored in the database and mined for association rules contain the data items beer and nuts and that 87% of the transactions that have the item beer also have the item nuts. The two percentage terms above are commonly referred to "support" and "confidence", respectively.
There are many prior art systems for generating association rules or "mining" data for association rules. However, these systems do not allow for the mining of association rules within user specified time intervals or calendars such as, "first day of the month", or "government paydays". Thus the variance of association rules over time given such a user defined calendar cannot be discovered using prior art methods. More specifically, the prior art methods handle the transaction data as one large segment and do not permit segmentation of the data so as to allow the above queries. For example, a user could not determine which part of the day the most transactions occurred with respect to beer and nuts. That is, analysis cannot be done of the data in finer time granularity may reveal that the association rule exists only in certain time intervals and does not occur in the remaining time intervals.
Accordingly, there is a need to provide a method for mining for association rules where there is a temporal component, specifically, a user defined calendar. Generating these calendric association rules allows the user to do a more detailed analysis of the transactions, and correspondingly provides the user with a more powerful tool with which to control business operations more efficiently.